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In summary, Fig. 3 indicates that exploratory motor babbling drastically decreased the learning time. Figure 4 shows the angle of crank turning and door opening/closing after 2350 and 3540 iterations, respectively. These iteration numbers correspond to one of the evaluated times in Fig. 3 at which the target motions had been generated correctly with exploratory motor babbling. The joint angles of the teaching signal 32 K. Takahashi et al. Fig. 4. Generated motions of crank turning and door opening/closing after 2350 and 3540 iterations (Left: crank turning, Right: door opening/closing) and the motion learned with exploratory motor babbling are similar.

3. Direction diﬀerence quantization The above-mentioned direction diﬀerence is shown as formula 9, the result are determined by the current pixel direction value and the mean value of its four neigh‐ borhood which has been ﬁred, is the absolute value of their diﬀerence. Then through the formula 9 processing. We get the 0 ~ 1 monotonically increasing direction diﬀerence . (9) Step3: Unit-linking PCNN fusing direction features: input to F channel is direction Moving Target Tracking Based on PCNN and Optical Flow 21 diﬀerence between current pixel’s direction value and the mean of its 4-neighborhood direction values whose ﬁre set is 1 (shown in Eq.

Diﬀerent tones (grass and load) cannot be ﬁltered out all in the original topological channel, which leads to pedestrians’ sinking into grass. By inputting intensity diﬀerence into F channel of PCNN, improved topo‐ logical channel ﬁlter grass and load successfully. Improvement is done to more accu‐ rately represent connectivity of topology. Fig. 6. Examples of improved topological channel 3 Algorithm Structure The step of proposed model. Step1: Grayscale image changed from color video frame is resized to 64*64.